Evidence map›Paper›PMID 42177562›Full record

SynthesisArchives of public health = Archives belges de sante publique2026

A scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned.

Roel Jude Bagaforo, Marie-Cécile Dupas, Steven Abrams, Simon Dellicour, Niel Hens

Abstract readSystematic Review
In one paragraph

Synthesis in Archives of public health = Archives belges de sante publique, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Roel Jude BagaforoData Science Institute (DSI), Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium. roeljude.bagaforo@uhasselt.be.
Marie-Cécile DupasData Science Institute (DSI), Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium. mariececile.dupas@ulb.be.
Steven AbramsData Science Institute (DSI), Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium.
Simon DellicourSpatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium.
Niel HensData Science Institute (DSI), Interuniversity Institute of Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium.

Funding

Belgian Federal Science Policy Office TD/231/BE-PINFonds Wetenschappelijk Onderzoek G0A4624N
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic underscored the importance of integrating human behaviour in infectious disease modelling approaches, yet an in-depth assessment of how behavioural components are incorporated remains limited. We conducted a scoping review of COVID-19 models applied to Belgian data to examine how behavioural dynamics, both voluntary and policy-driven, were represented within model structures. Our aim was to identify current practices, highlight methodological gaps, and provide recommendations for the development of behaviourally integrated epidemiological models.

methodsUsing Scopus and PubMed, we identified 98 studies published between March 2020 and October 2024, describing 105 models in total. Models were classified by model class (mathematical, statistical, or ensemble), objectives, approaches used to incorporate behavioural factors, and types of behaviour data employed.

resultsBehavioural integration was confined to specific modelling contexts, with only half of the 105 models incorporating behavioural components. Mechanistic models, particularly compartmental models, were the most likely to include behavioural features, especially in studies assessing non-pharmaceutical interventions or conducting long-term forecasts and scenario analyses. Behavioural change was most commonly represented through modifications to transmission parameters or contact matrices. These adjustments were frequently informed by social contact surveys or mobility data derived from various sources.

conclusionsIn contrast to previous reviews that focused exclusively on behavioural models, this study evaluates the full landscape of Belgian COVID-19 models, offering a comprehensive perspective on how behavioural representation varies across modelling approaches. Our findings recommend that effective behavioural integration relies on timely, routine, and disaggregated surveillance and behaviour data, alongside the use of flexible mechanistic models.

Indexed as

BehaviourEnsemble modelsEpidemiological dynamicsMathematical modelsMechanistic modelsNon-pharmaceutical interventionsPharmaceutical interventionsSARS-CoV-2Statistical models

Identifiers

PMID42177562
PMCPMC13383522

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.